Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition.

IF 3.6 3区 生物学 Q1 BIOLOGY
Nazareno Gonzalez, Melanie Perez Küper, Matias Garcia Fallit, Alejandro J Nicola Candia, Jorge A Peña Agudelo, Maicol Suarez Velandia, Ana Clara Romero, Guillermo Agustin Videla-Richardson, Marianela Candolfi
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引用次数: 0

Abstract

Glioblastoma (GBM) presents significant therapeutic challenges due to its invasive nature and resistance to standard chemotherapy, i.e., temozolomide (TMZ). This study aimed to identify gene signatures that predict poor TMZ response and high PD-L1/PD-1 tumor expression, and explore potential sensitivity to alternative drugs. We analyzed The Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes (DEGs) linked to these characteristics. Among 33 upregulated DEGs, 5 were significantly correlated with overall survival. A risk score model was built using these 5 DEGs, classifying patients into low-, medium-, and high-risk groups. We assessed immune cell infiltration, immunosuppressive mediators, and epithelial-mesenchymal transition (EMT) markers in each group using correlation analysis, Gene Set Enrichment Analysis (GSEA), and machine learning. The model demonstrated strong predictive power, with high-risk patients exhibiting poorer survival and increased immune infiltration. GSEA revealed upregulation of immune and EMT-related pathways in high-risk patients. Our analyses suggest that high-risk patients may exhibit limited response to PD-1 inhibitors, but could show sensitivity to etoposide and paclitaxel. This risk score model provides a valuable tool for guiding therapeutic decisions and identifying alternative chemotherapy options to enable the development of personalized and cost-effective treatments for GBM patients.

预测胶质母细胞瘤的治疗结果:TMZ耐药和免疫检查点抑制的风险评分模型
胶质母细胞瘤(GBM)由于其侵袭性和对标准化疗(即替莫唑胺(TMZ))的耐药性,提出了重大的治疗挑战。本研究旨在鉴定预测TMZ不良反应和PD-L1/PD-1肿瘤高表达的基因特征,并探索对替代药物的潜在敏感性。我们分析了癌症基因组图谱(TCGA)活检数据,以确定与这些特征相关的差异表达基因(DEGs)。在33个上调的deg中,有5个与总生存率显著相关。使用这5个deg建立风险评分模型,将患者分为低、中、高风险组。我们使用相关性分析、基因集富集分析(GSEA)和机器学习来评估各组的免疫细胞浸润、免疫抑制介质和上皮-间质转化(EMT)标志物。该模型显示出很强的预测能力,高危患者生存率较差,免疫浸润增加。GSEA显示高危患者免疫和emt相关通路上调。我们的分析表明,高风险患者对PD-1抑制剂的反应可能有限,但对依托泊苷和紫杉醇可能敏感。该风险评分模型为指导治疗决策和确定替代化疗方案提供了有价值的工具,从而能够为GBM患者开发个性化和具有成本效益的治疗方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biology-Basel
Biology-Basel Biological Science-Biological Science
CiteScore
5.70
自引率
4.80%
发文量
1618
审稿时长
11 weeks
期刊介绍: Biology (ISSN 2079-7737) is an international, peer-reviewed, quick-refereeing open access journal of Biological Science published by MDPI online. It publishes reviews, research papers and communications in all areas of biology and at the interface of related disciplines. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files regarding the full details of the experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
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